Rifat, Rakib HossainSiddique S.Das L.R.Haque M.A.2026-08-172026-08-172023-01-01R. H. Rifat, S. Siddique, L. R. Das and M. A. Haque, "Facial Shape-Based Eyeglass Recommendation Using Convolutional Neural Networks," 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico, 2023, pp. 867-872, doi: 10.1109/SSCI52147.2023.10371836.97816654306542-s2.0-85182919834https://hdl.handle.net/10361/29213Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape; to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear.867-872en-USfalseCNNDenseNet121Eye glassInceptionV3InceptionV4ResNet50Trasnfer learningVGG16Vit smallFace--Computer simulation.Neural networks (Computer science).Conference Proceeding10.1109/SSCI52147.2023.10371836